Computational thinking for using models of water flow in environmental systems: Intertwining three dimensions in a learning progression

Abstract

Nearly a decade ago, the Framework for K‐12 Science Education argued for the need to intertwine science and engineering practices, disciplinary core ideas, and crosscutting concepts in performance expectations. However, there are few empirical examples for how intertwining three dimensions facilitates learning. In this study, we used a learning progressions approach to examine how student engagement in computational thinking (science and engineering practice) intertwines with learning about the flow of water through environmental systems (disciplinary core ideas) and understanding of systems and system models (crosscutting concept). We developed three secondary‐level curriculum units situated in current groundwater contamination and urban flooding contexts. Units included specially designed NetLogo computational models. Post‐assessments measured student performances in computational thinking processes and understanding of hydrologic systems. Using item response theory in our analysis, we identified distinct levels of performance on a learning progression. At the lower end, literal model users interacted with models and manipulated model interfaces to achieve a specified goal. In the middle, Model Technicians used computational models to solve real‐world problems. At the upper end, principle‐based model users used computational thinking processes and principles related to systems modeling and hydrology to explain how the models worked to predict water flow. Differences between performances of literal model users, model technicians, and principle‐based model users reflected shifts in how students made sense of the systems and system models crosscutting concept. These shifts in performances aligned with progress in computational thinking practices and finally with use of hydrology disciplinary core ideas. These findings contribute to understanding of how science and engineering practices, disciplinary core ideas, and crosscutting concepts intertwine during learning; how computational thinking practices develop; and how computational thinking about system models facilitates learning for environmental science literacy.

How do thinking styles and STEM attitudes have effects on computational thinking? A structural equation modeling analysis

“…The interaction between CT and STEM also provides new insight into the construct, nature, and definition of CT. Wing (2006) stated that CT “complements and combines mathematical and engineering thinking” (p. 35). It is also pointed out that CT skills are required by and can be learned in various disciplines (Sung & Black, 2020), especially the STEM disciplines (e.g., Chen & Terada, 2021; Gunckel et al, 2022; Kite & Park, 2023; Lilly et al, 2022; NGSS Lead States, 2013; Peters‐Burton et al, 2023; Rachmatullah & Wiebe, 2022). For instance, The Next Generation Science Standards (NGSS) advocate using CT to develop scientific understanding (NGSS Lead States, 2013).…”

“…Recently, much research has highlighted the critical importance of CT (e.g., Dagli & Tokmak, 2021; Grizioti & Kynigos, 2021; Guggemos, 2021; International Society for Technology in Education, 2016; Kynigos & Grizioti, 2020; Tikva & Tambouris, 2021; Wing, 2006). In response to the constantly evolving needs for computationally literate workforces (Asbell‐Clarke et al, 2021; Rowe et al, 2021; Song et al, 2021), CT has been integrated into the curricula (e.g., Lei et al, 2020; Sun, Hu, & Zhou, 2021), especially the science, technology, engineering and mathematics (STEM) curricula (e.g., Chen & Terada, 2021; Gunckel et al, 2022; Kite & Park, 2023; Lilly et al, 2022; NGSS Lead States, 2013; Peters‐Burton et al, 2023; Rachmatullah & Wiebe, 2022) of many countries.…”

Computational Thinking to Learn Environmental Sustainability: A Learning Progression

“…The interdisciplinary nature of CT and having the potential to emphasize critical connections for ES students emphasize sustainability (Schoolman et al, 2012) and improve technological competency at a variety of levels. A gap in research exists in combining CT with ES overall (Gunckel et al, 2022), but also particularly considering the interdisciplinary theme of sustainability combined with computational complexity. For example, modern economics has not considered that economic benefits have natural costs and if technology is reoriented, environmental solutions are feasible (Valero et al, 2021).…”